Senior Data Platform Engineer

Inspiren

4d•$180,000 - $200,000•Remote

About The Position

From our realtime monitoring platform to deeper longitudinal insights, data is at the heart of everything Inspiren does. We are seeking a highly skilled Senior Data Platform Engineer to own, maintain, and develop our data infrastructure built on Databricks and AWS to scale and accelerate our data capabilities across the company.

Requirements

Bachelor's or Master's degree in Computer Science, or a related engineering field.
5+ years of experience in data infrastructure, platform engineering, data engineering, or similar.
Cloud Providers: Demonstrated hands-on experience with Databricks. AWS experience is a plus but not required.
Data Warehousing: Expertise in modern data warehouse and lakehouse architectures.
Data Pipelines: Expertise in modern ETL technologies and building and supporting data pipelines at scale.
Access Control and Management: Familiarity with access control, data governance, and security.
Reliability: Demonstrated experience of reliability, observability, and monitoring of infrastructure and systems.
DevEx: Experience building and maintaining data platforms with a wide variety of stakeholders/users with a diversity of technical expertises.
Scalability: Demonstrated ability to scale cloud-based data workloads, whilst optimizing and monitoring cost
Communication: Excellent verbal and written communication skills, with the ability to convey complex ideas clearly.
Adaptability: Comfortable working in a fast-paced, dynamic environment and adapting to changing priorities.

Nice To Haves

Start-up experience is a plus.
Health-tech experience is a plus.

Responsibilities

Collaborate with engineering, data science, ML, data engineering, and product analytics teams to understand and shape the future needs of our data platform and infrastructure.
Define, drive, and implement the future live ingestion layer of data into our data platform (e.g. Kafka, Kinesis).
Define and evolve standards for storage, compute, data management, provenance, and orchestration.
Monitor current and forecast future infrastructure needs and costs based on business requirements.
Implement and manage role-based access management.
Design and implement reusable tooling and frameworks for data consumers (e.g., data quality monitoring frameworks, templated pipelines, foolproof backfilling capabilities).